{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from sklearn.datasets import load_wine\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "wine = load_wine()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.640000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.380000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.680000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.320000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14.20</td>\n",
       "      <td>1.76</td>\n",
       "      <td>2.45</td>\n",
       "      <td>15.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>3.27</td>\n",
       "      <td>3.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1450.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14.39</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.45</td>\n",
       "      <td>14.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.250000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1290.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.06</td>\n",
       "      <td>2.15</td>\n",
       "      <td>2.61</td>\n",
       "      <td>17.6</td>\n",
       "      <td>121.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.51</td>\n",
       "      <td>0.31</td>\n",
       "      <td>1.25</td>\n",
       "      <td>5.050000</td>\n",
       "      <td>1.06</td>\n",
       "      <td>3.58</td>\n",
       "      <td>1295.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>14.83</td>\n",
       "      <td>1.64</td>\n",
       "      <td>2.17</td>\n",
       "      <td>14.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.98</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.98</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>1.08</td>\n",
       "      <td>2.85</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13.86</td>\n",
       "      <td>1.35</td>\n",
       "      <td>2.27</td>\n",
       "      <td>16.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.98</td>\n",
       "      <td>3.15</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.85</td>\n",
       "      <td>7.220000</td>\n",
       "      <td>1.01</td>\n",
       "      <td>3.55</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14.10</td>\n",
       "      <td>2.16</td>\n",
       "      <td>2.30</td>\n",
       "      <td>18.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.32</td>\n",
       "      <td>0.22</td>\n",
       "      <td>2.38</td>\n",
       "      <td>5.750000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1510.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.12</td>\n",
       "      <td>1.48</td>\n",
       "      <td>2.32</td>\n",
       "      <td>16.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.57</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.41</td>\n",
       "      <td>16.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1.81</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>1.15</td>\n",
       "      <td>2.90</td>\n",
       "      <td>1320.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14.75</td>\n",
       "      <td>1.73</td>\n",
       "      <td>2.39</td>\n",
       "      <td>11.4</td>\n",
       "      <td>91.0</td>\n",
       "      <td>3.10</td>\n",
       "      <td>3.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>2.73</td>\n",
       "      <td>1150.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14.38</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.38</td>\n",
       "      <td>12.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.64</td>\n",
       "      <td>0.29</td>\n",
       "      <td>2.96</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>1.20</td>\n",
       "      <td>3.00</td>\n",
       "      <td>1547.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>13.63</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.70</td>\n",
       "      <td>17.2</td>\n",
       "      <td>112.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.91</td>\n",
       "      <td>0.30</td>\n",
       "      <td>1.46</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.88</td>\n",
       "      <td>1310.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>14.30</td>\n",
       "      <td>1.92</td>\n",
       "      <td>2.72</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.14</td>\n",
       "      <td>0.33</td>\n",
       "      <td>1.97</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>1.07</td>\n",
       "      <td>2.65</td>\n",
       "      <td>1280.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.83</td>\n",
       "      <td>1.57</td>\n",
       "      <td>2.62</td>\n",
       "      <td>20.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>3.40</td>\n",
       "      <td>0.40</td>\n",
       "      <td>1.72</td>\n",
       "      <td>6.600000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1130.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14.19</td>\n",
       "      <td>1.59</td>\n",
       "      <td>2.48</td>\n",
       "      <td>16.5</td>\n",
       "      <td>108.0</td>\n",
       "      <td>3.30</td>\n",
       "      <td>3.93</td>\n",
       "      <td>0.32</td>\n",
       "      <td>1.86</td>\n",
       "      <td>8.700000</td>\n",
       "      <td>1.23</td>\n",
       "      <td>2.82</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.64</td>\n",
       "      <td>3.10</td>\n",
       "      <td>2.56</td>\n",
       "      <td>15.2</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.70</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.17</td>\n",
       "      <td>1.66</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>3.36</td>\n",
       "      <td>845.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.06</td>\n",
       "      <td>1.63</td>\n",
       "      <td>2.28</td>\n",
       "      <td>16.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>3.17</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.10</td>\n",
       "      <td>5.650000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.71</td>\n",
       "      <td>780.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.93</td>\n",
       "      <td>3.80</td>\n",
       "      <td>2.65</td>\n",
       "      <td>18.6</td>\n",
       "      <td>102.0</td>\n",
       "      <td>2.41</td>\n",
       "      <td>2.41</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.52</td>\n",
       "      <td>770.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>13.71</td>\n",
       "      <td>1.86</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.61</td>\n",
       "      <td>2.88</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.69</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>1.11</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>12.85</td>\n",
       "      <td>1.60</td>\n",
       "      <td>2.52</td>\n",
       "      <td>17.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.48</td>\n",
       "      <td>2.37</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.930000</td>\n",
       "      <td>1.09</td>\n",
       "      <td>3.63</td>\n",
       "      <td>1015.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>13.50</td>\n",
       "      <td>1.81</td>\n",
       "      <td>2.61</td>\n",
       "      <td>20.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.53</td>\n",
       "      <td>2.61</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.66</td>\n",
       "      <td>3.520000</td>\n",
       "      <td>1.12</td>\n",
       "      <td>3.82</td>\n",
       "      <td>845.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13.05</td>\n",
       "      <td>2.05</td>\n",
       "      <td>3.22</td>\n",
       "      <td>25.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>2.63</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.92</td>\n",
       "      <td>3.580000</td>\n",
       "      <td>1.13</td>\n",
       "      <td>3.20</td>\n",
       "      <td>830.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13.39</td>\n",
       "      <td>1.77</td>\n",
       "      <td>2.62</td>\n",
       "      <td>16.1</td>\n",
       "      <td>93.0</td>\n",
       "      <td>2.85</td>\n",
       "      <td>2.94</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.45</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>0.92</td>\n",
       "      <td>3.22</td>\n",
       "      <td>1195.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13.30</td>\n",
       "      <td>1.72</td>\n",
       "      <td>2.14</td>\n",
       "      <td>17.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>2.40</td>\n",
       "      <td>2.19</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.35</td>\n",
       "      <td>3.950000</td>\n",
       "      <td>1.02</td>\n",
       "      <td>2.77</td>\n",
       "      <td>1285.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>13.87</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.80</td>\n",
       "      <td>19.4</td>\n",
       "      <td>107.0</td>\n",
       "      <td>2.95</td>\n",
       "      <td>2.97</td>\n",
       "      <td>0.37</td>\n",
       "      <td>1.76</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>1.25</td>\n",
       "      <td>3.40</td>\n",
       "      <td>915.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14.02</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.21</td>\n",
       "      <td>16.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.33</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.98</td>\n",
       "      <td>4.700000</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.59</td>\n",
       "      <td>1035.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>13.32</td>\n",
       "      <td>3.24</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.5</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1.93</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.420000</td>\n",
       "      <td>0.55</td>\n",
       "      <td>1.62</td>\n",
       "      <td>650.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>13.08</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.36</td>\n",
       "      <td>21.5</td>\n",
       "      <td>113.0</td>\n",
       "      <td>1.41</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.14</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.33</td>\n",
       "      <td>550.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>13.50</td>\n",
       "      <td>3.12</td>\n",
       "      <td>2.62</td>\n",
       "      <td>24.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>1.40</td>\n",
       "      <td>1.57</td>\n",
       "      <td>0.22</td>\n",
       "      <td>1.25</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.30</td>\n",
       "      <td>500.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>12.79</td>\n",
       "      <td>2.67</td>\n",
       "      <td>2.48</td>\n",
       "      <td>22.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>1.36</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1.26</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.47</td>\n",
       "      <td>480.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>13.11</td>\n",
       "      <td>1.90</td>\n",
       "      <td>2.75</td>\n",
       "      <td>25.5</td>\n",
       "      <td>116.0</td>\n",
       "      <td>2.20</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.56</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.33</td>\n",
       "      <td>425.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>13.23</td>\n",
       "      <td>3.30</td>\n",
       "      <td>2.28</td>\n",
       "      <td>18.5</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.87</td>\n",
       "      <td>10.520000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.51</td>\n",
       "      <td>675.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>12.58</td>\n",
       "      <td>1.29</td>\n",
       "      <td>2.10</td>\n",
       "      <td>20.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.40</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.58</td>\n",
       "      <td>1.55</td>\n",
       "      <td>640.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>13.17</td>\n",
       "      <td>5.19</td>\n",
       "      <td>2.32</td>\n",
       "      <td>22.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.55</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.48</td>\n",
       "      <td>725.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>13.84</td>\n",
       "      <td>4.12</td>\n",
       "      <td>2.38</td>\n",
       "      <td>19.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.56</td>\n",
       "      <td>9.010000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.64</td>\n",
       "      <td>480.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>12.45</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.64</td>\n",
       "      <td>27.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.63</td>\n",
       "      <td>1.14</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.73</td>\n",
       "      <td>880.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>14.34</td>\n",
       "      <td>1.68</td>\n",
       "      <td>2.70</td>\n",
       "      <td>25.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.31</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2.70</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.96</td>\n",
       "      <td>660.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>13.48</td>\n",
       "      <td>1.67</td>\n",
       "      <td>2.64</td>\n",
       "      <td>22.5</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.60</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.52</td>\n",
       "      <td>2.29</td>\n",
       "      <td>11.750000</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.78</td>\n",
       "      <td>620.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>160</th>\n",
       "      <td>12.36</td>\n",
       "      <td>3.83</td>\n",
       "      <td>2.38</td>\n",
       "      <td>21.0</td>\n",
       "      <td>88.0</td>\n",
       "      <td>2.30</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.04</td>\n",
       "      <td>7.650000</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.58</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>13.69</td>\n",
       "      <td>3.26</td>\n",
       "      <td>2.54</td>\n",
       "      <td>20.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>1.83</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.80</td>\n",
       "      <td>5.880000</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.82</td>\n",
       "      <td>680.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>12.85</td>\n",
       "      <td>3.27</td>\n",
       "      <td>2.58</td>\n",
       "      <td>22.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.96</td>\n",
       "      <td>5.580000</td>\n",
       "      <td>0.87</td>\n",
       "      <td>2.11</td>\n",
       "      <td>570.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>12.96</td>\n",
       "      <td>3.45</td>\n",
       "      <td>2.35</td>\n",
       "      <td>18.5</td>\n",
       "      <td>106.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.94</td>\n",
       "      <td>5.280000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>1.75</td>\n",
       "      <td>675.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>13.78</td>\n",
       "      <td>2.76</td>\n",
       "      <td>2.30</td>\n",
       "      <td>22.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>1.35</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.41</td>\n",
       "      <td>1.03</td>\n",
       "      <td>9.580000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.68</td>\n",
       "      <td>615.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>13.73</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.26</td>\n",
       "      <td>22.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.28</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.15</td>\n",
       "      <td>6.620000</td>\n",
       "      <td>0.78</td>\n",
       "      <td>1.75</td>\n",
       "      <td>520.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>13.45</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.60</td>\n",
       "      <td>23.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.46</td>\n",
       "      <td>10.680000</td>\n",
       "      <td>0.85</td>\n",
       "      <td>1.56</td>\n",
       "      <td>695.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>12.82</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.30</td>\n",
       "      <td>19.5</td>\n",
       "      <td>88.0</td>\n",
       "      <td>1.48</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.97</td>\n",
       "      <td>10.260000</td>\n",
       "      <td>0.72</td>\n",
       "      <td>1.75</td>\n",
       "      <td>685.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>13.58</td>\n",
       "      <td>2.58</td>\n",
       "      <td>2.69</td>\n",
       "      <td>24.5</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1.55</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.54</td>\n",
       "      <td>8.660000</td>\n",
       "      <td>0.74</td>\n",
       "      <td>1.80</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>13.40</td>\n",
       "      <td>4.60</td>\n",
       "      <td>2.86</td>\n",
       "      <td>25.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>1.98</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.27</td>\n",
       "      <td>1.11</td>\n",
       "      <td>8.500000</td>\n",
       "      <td>0.67</td>\n",
       "      <td>1.92</td>\n",
       "      <td>630.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>12.20</td>\n",
       "      <td>3.03</td>\n",
       "      <td>2.32</td>\n",
       "      <td>19.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>1.25</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.73</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.83</td>\n",
       "      <td>510.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>171</th>\n",
       "      <td>12.77</td>\n",
       "      <td>2.39</td>\n",
       "      <td>2.28</td>\n",
       "      <td>19.5</td>\n",
       "      <td>86.0</td>\n",
       "      <td>1.39</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.64</td>\n",
       "      <td>9.899999</td>\n",
       "      <td>0.57</td>\n",
       "      <td>1.63</td>\n",
       "      <td>470.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172</th>\n",
       "      <td>14.16</td>\n",
       "      <td>2.51</td>\n",
       "      <td>2.48</td>\n",
       "      <td>20.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1.24</td>\n",
       "      <td>9.700000</td>\n",
       "      <td>0.62</td>\n",
       "      <td>1.71</td>\n",
       "      <td>660.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.300000</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.200000</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        0     1     2     3      4     5     6     7     8          9     10  \\\n",
       "0    14.23  1.71  2.43  15.6  127.0  2.80  3.06  0.28  2.29   5.640000  1.04   \n",
       "1    13.20  1.78  2.14  11.2  100.0  2.65  2.76  0.26  1.28   4.380000  1.05   \n",
       "2    13.16  2.36  2.67  18.6  101.0  2.80  3.24  0.30  2.81   5.680000  1.03   \n",
       "3    14.37  1.95  2.50  16.8  113.0  3.85  3.49  0.24  2.18   7.800000  0.86   \n",
       "4    13.24  2.59  2.87  21.0  118.0  2.80  2.69  0.39  1.82   4.320000  1.04   \n",
       "5    14.20  1.76  2.45  15.2  112.0  3.27  3.39  0.34  1.97   6.750000  1.05   \n",
       "6    14.39  1.87  2.45  14.6   96.0  2.50  2.52  0.30  1.98   5.250000  1.02   \n",
       "7    14.06  2.15  2.61  17.6  121.0  2.60  2.51  0.31  1.25   5.050000  1.06   \n",
       "8    14.83  1.64  2.17  14.0   97.0  2.80  2.98  0.29  1.98   5.200000  1.08   \n",
       "9    13.86  1.35  2.27  16.0   98.0  2.98  3.15  0.22  1.85   7.220000  1.01   \n",
       "10   14.10  2.16  2.30  18.0  105.0  2.95  3.32  0.22  2.38   5.750000  1.25   \n",
       "11   14.12  1.48  2.32  16.8   95.0  2.20  2.43  0.26  1.57   5.000000  1.17   \n",
       "12   13.75  1.73  2.41  16.0   89.0  2.60  2.76  0.29  1.81   5.600000  1.15   \n",
       "13   14.75  1.73  2.39  11.4   91.0  3.10  3.69  0.43  2.81   5.400000  1.25   \n",
       "14   14.38  1.87  2.38  12.0  102.0  3.30  3.64  0.29  2.96   7.500000  1.20   \n",
       "15   13.63  1.81  2.70  17.2  112.0  2.85  2.91  0.30  1.46   7.300000  1.28   \n",
       "16   14.30  1.92  2.72  20.0  120.0  2.80  3.14  0.33  1.97   6.200000  1.07   \n",
       "17   13.83  1.57  2.62  20.0  115.0  2.95  3.40  0.40  1.72   6.600000  1.13   \n",
       "18   14.19  1.59  2.48  16.5  108.0  3.30  3.93  0.32  1.86   8.700000  1.23   \n",
       "19   13.64  3.10  2.56  15.2  116.0  2.70  3.03  0.17  1.66   5.100000  0.96   \n",
       "20   14.06  1.63  2.28  16.0  126.0  3.00  3.17  0.24  2.10   5.650000  1.09   \n",
       "21   12.93  3.80  2.65  18.6  102.0  2.41  2.41  0.25  1.98   4.500000  1.03   \n",
       "22   13.71  1.86  2.36  16.6  101.0  2.61  2.88  0.27  1.69   3.800000  1.11   \n",
       "23   12.85  1.60  2.52  17.8   95.0  2.48  2.37  0.26  1.46   3.930000  1.09   \n",
       "24   13.50  1.81  2.61  20.0   96.0  2.53  2.61  0.28  1.66   3.520000  1.12   \n",
       "25   13.05  2.05  3.22  25.0  124.0  2.63  2.68  0.47  1.92   3.580000  1.13   \n",
       "26   13.39  1.77  2.62  16.1   93.0  2.85  2.94  0.34  1.45   4.800000  0.92   \n",
       "27   13.30  1.72  2.14  17.0   94.0  2.40  2.19  0.27  1.35   3.950000  1.02   \n",
       "28   13.87  1.90  2.80  19.4  107.0  2.95  2.97  0.37  1.76   4.500000  1.25   \n",
       "29   14.02  1.68  2.21  16.0   96.0  2.65  2.33  0.26  1.98   4.700000  1.04   \n",
       "..     ...   ...   ...   ...    ...   ...   ...   ...   ...        ...   ...   \n",
       "148  13.32  3.24  2.38  21.5   92.0  1.93  0.76  0.45  1.25   8.420000  0.55   \n",
       "149  13.08  3.90  2.36  21.5  113.0  1.41  1.39  0.34  1.14   9.400000  0.57   \n",
       "150  13.50  3.12  2.62  24.0  123.0  1.40  1.57  0.22  1.25   8.600000  0.59   \n",
       "151  12.79  2.67  2.48  22.0  112.0  1.48  1.36  0.24  1.26  10.800000  0.48   \n",
       "152  13.11  1.90  2.75  25.5  116.0  2.20  1.28  0.26  1.56   7.100000  0.61   \n",
       "153  13.23  3.30  2.28  18.5   98.0  1.80  0.83  0.61  1.87  10.520000  0.56   \n",
       "154  12.58  1.29  2.10  20.0  103.0  1.48  0.58  0.53  1.40   7.600000  0.58   \n",
       "155  13.17  5.19  2.32  22.0   93.0  1.74  0.63  0.61  1.55   7.900000  0.60   \n",
       "156  13.84  4.12  2.38  19.5   89.0  1.80  0.83  0.48  1.56   9.010000  0.57   \n",
       "157  12.45  3.03  2.64  27.0   97.0  1.90  0.58  0.63  1.14   7.500000  0.67   \n",
       "158  14.34  1.68  2.70  25.0   98.0  2.80  1.31  0.53  2.70  13.000000  0.57   \n",
       "159  13.48  1.67  2.64  22.5   89.0  2.60  1.10  0.52  2.29  11.750000  0.57   \n",
       "160  12.36  3.83  2.38  21.0   88.0  2.30  0.92  0.50  1.04   7.650000  0.56   \n",
       "161  13.69  3.26  2.54  20.0  107.0  1.83  0.56  0.50  0.80   5.880000  0.96   \n",
       "162  12.85  3.27  2.58  22.0  106.0  1.65  0.60  0.60  0.96   5.580000  0.87   \n",
       "163  12.96  3.45  2.35  18.5  106.0  1.39  0.70  0.40  0.94   5.280000  0.68   \n",
       "164  13.78  2.76  2.30  22.0   90.0  1.35  0.68  0.41  1.03   9.580000  0.70   \n",
       "165  13.73  4.36  2.26  22.5   88.0  1.28  0.47  0.52  1.15   6.620000  0.78   \n",
       "166  13.45  3.70  2.60  23.0  111.0  1.70  0.92  0.43  1.46  10.680000  0.85   \n",
       "167  12.82  3.37  2.30  19.5   88.0  1.48  0.66  0.40  0.97  10.260000  0.72   \n",
       "168  13.58  2.58  2.69  24.5  105.0  1.55  0.84  0.39  1.54   8.660000  0.74   \n",
       "169  13.40  4.60  2.86  25.0  112.0  1.98  0.96  0.27  1.11   8.500000  0.67   \n",
       "170  12.20  3.03  2.32  19.0   96.0  1.25  0.49  0.40  0.73   5.500000  0.66   \n",
       "171  12.77  2.39  2.28  19.5   86.0  1.39  0.51  0.48  0.64   9.899999  0.57   \n",
       "172  14.16  2.51  2.48  20.0   91.0  1.68  0.70  0.44  1.24   9.700000  0.62   \n",
       "173  13.71  5.65  2.45  20.5   95.0  1.68  0.61  0.52  1.06   7.700000  0.64   \n",
       "174  13.40  3.91  2.48  23.0  102.0  1.80  0.75  0.43  1.41   7.300000  0.70   \n",
       "175  13.27  4.28  2.26  20.0  120.0  1.59  0.69  0.43  1.35  10.200000  0.59   \n",
       "176  13.17  2.59  2.37  20.0  120.0  1.65  0.68  0.53  1.46   9.300000  0.60   \n",
       "177  14.13  4.10  2.74  24.5   96.0  2.05  0.76  0.56  1.35   9.200000  0.61   \n",
       "\n",
       "       11      12  0   \n",
       "0    3.92  1065.0   0  \n",
       "1    3.40  1050.0   0  \n",
       "2    3.17  1185.0   0  \n",
       "3    3.45  1480.0   0  \n",
       "4    2.93   735.0   0  \n",
       "5    2.85  1450.0   0  \n",
       "6    3.58  1290.0   0  \n",
       "7    3.58  1295.0   0  \n",
       "8    2.85  1045.0   0  \n",
       "9    3.55  1045.0   0  \n",
       "10   3.17  1510.0   0  \n",
       "11   2.82  1280.0   0  \n",
       "12   2.90  1320.0   0  \n",
       "13   2.73  1150.0   0  \n",
       "14   3.00  1547.0   0  \n",
       "15   2.88  1310.0   0  \n",
       "16   2.65  1280.0   0  \n",
       "17   2.57  1130.0   0  \n",
       "18   2.82  1680.0   0  \n",
       "19   3.36   845.0   0  \n",
       "20   3.71   780.0   0  \n",
       "21   3.52   770.0   0  \n",
       "22   4.00  1035.0   0  \n",
       "23   3.63  1015.0   0  \n",
       "24   3.82   845.0   0  \n",
       "25   3.20   830.0   0  \n",
       "26   3.22  1195.0   0  \n",
       "27   2.77  1285.0   0  \n",
       "28   3.40   915.0   0  \n",
       "29   3.59  1035.0   0  \n",
       "..    ...     ...  ..  \n",
       "148  1.62   650.0   2  \n",
       "149  1.33   550.0   2  \n",
       "150  1.30   500.0   2  \n",
       "151  1.47   480.0   2  \n",
       "152  1.33   425.0   2  \n",
       "153  1.51   675.0   2  \n",
       "154  1.55   640.0   2  \n",
       "155  1.48   725.0   2  \n",
       "156  1.64   480.0   2  \n",
       "157  1.73   880.0   2  \n",
       "158  1.96   660.0   2  \n",
       "159  1.78   620.0   2  \n",
       "160  1.58   520.0   2  \n",
       "161  1.82   680.0   2  \n",
       "162  2.11   570.0   2  \n",
       "163  1.75   675.0   2  \n",
       "164  1.68   615.0   2  \n",
       "165  1.75   520.0   2  \n",
       "166  1.56   695.0   2  \n",
       "167  1.75   685.0   2  \n",
       "168  1.80   750.0   2  \n",
       "169  1.92   630.0   2  \n",
       "170  1.83   510.0   2  \n",
       "171  1.63   470.0   2  \n",
       "172  1.71   660.0   2  \n",
       "173  1.74   740.0   2  \n",
       "174  1.56   750.0   2  \n",
       "175  1.56   835.0   2  \n",
       "176  1.62   840.0   2  \n",
       "177  1.60   560.0   2  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.concat([pd.DataFrame(wine.data),pd.DataFrame(wine.target)],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alcohol',\n",
       " 'malic_acid',\n",
       " 'ash',\n",
       " 'alcalinity_of_ash',\n",
       " 'magnesium',\n",
       " 'total_phenols',\n",
       " 'flavanoids',\n",
       " 'nonflavanoid_phenols',\n",
       " 'proanthocyanins',\n",
       " 'color_intensity',\n",
       " 'hue',\n",
       " 'od280/od315_of_diluted_wines',\n",
       " 'proline']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['class_0', 'class_1', 'class_2'], dtype='<U7')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(124, 13)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(54, 13)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Xtest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 2, 2, 1, 0, 0, 2, 2, 0, 1, 2, 1, 1, 1, 1, 1, 2, 0, 1, 0,\n",
       "       1, 2, 2, 0, 2, 1, 1, 0, 1, 2, 0, 2, 1, 2, 0, 2, 0, 0, 2, 2, 0, 2,\n",
       "       0, 1, 2, 1, 2, 0, 2, 2, 0, 0, 2, 0, 1, 2, 2, 1, 2, 2, 0, 0, 1, 1,\n",
       "       2, 1, 0, 1, 2, 0, 2, 2, 0, 0, 2, 0, 1, 1, 1, 1, 1, 1, 0, 2, 1, 1,\n",
       "       1, 2, 2, 2, 1, 2, 1, 0, 1, 1, 2, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 2,\n",
       "       0, 2, 0, 0, 1, 0, 1, 2, 0, 1, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ytrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\")\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score = clf.score(Xtest, Ytest) #返回预测的准确度accuracy\n",
    "\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_name = ['酒精','苹果酸','灰','灰的碱性','镁','总酚','类黄酮','非黄烷类酚类','花青素','颜色强度','色调','od280/od315稀释葡萄酒','脯氨酸']\n",
    "\n",
    "import graphviz\n",
    "dot_data = tree.export_graphviz(clf, out_file=\"Tree.dot\"\n",
    "                                ,feature_names = feature_name\n",
    "                                ,class_names=[\"琴酒\",\"雪莉\",\"贝尔摩德\"]\n",
    "                                ,filled=True\n",
    "                                ,rounded=True\n",
    "                               ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Microsoft Windows [版本 10.0.16299.726]\r\n",
      "(c) 2017 Microsoft Corporation。保留所有权利。\r\n",
      "\r\n",
      "C:\\Users\\Administrator\\Desktop\\sklearn\\01 决策树课件数据源码>dot -Tjpg Tree.txt -o tree.jpg\n",
      "\r\n",
      "C:\\Users\\Administrator\\Desktop\\sklearn\\01 决策树课件数据源码>"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Error: dot: can't open Tree.txt\r\n"
     ]
    }
   ],
   "source": [
    "%%cmd\n",
    "dot -Tjpg Tree.txt -o tree.jpg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.36380815, 0.        , 0.        , 0.43065022,\n",
       "       0.0332603 , 0.07281671, 0.09946463])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('酒精', 0.0),\n",
       " ('苹果酸', 0.0),\n",
       " ('灰', 0.0),\n",
       " ('灰的碱性', 0.0),\n",
       " ('镁', 0.0),\n",
       " ('总酚', 0.0),\n",
       " ('类黄酮', 0.3638081458360176),\n",
       " ('非黄烷类酚类', 0.0),\n",
       " ('花青素', 0.0),\n",
       " ('颜色强度', 0.43065022117565915),\n",
       " ('色调', 0.03326029751066887),\n",
       " ('od280/od315稀释葡萄酒', 0.07281670605675937),\n",
       " ('脯氨酸', 0.09946462942089493)]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[*zip(feature_name,clf.feature_importances_)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9629629629629629"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\",random_state=30)\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score = clf.score(Xtest, Ytest) #返回预测的准确度\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9814814814814815"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\"\n",
    "                                    ,random_state=30\n",
    "                                    ,splitter=\"random\"\n",
    "                                    )\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "score = clf.score(Xtest, Ytest)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"middle\" x=\"458\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">色调 &lt;= 0.828</text>\r\n",
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       "<g id=\"node16\" class=\"node\"><title>15</title>\r\n",
       "<path fill=\"#39e581\" fill-opacity=\"0.909804\" stroke=\"black\" d=\"M709.5,-544C709.5,-544 612.5,-544 612.5,-544 606.5,-544 600.5,-538 600.5,-532 600.5,-532 600.5,-473 600.5,-473 600.5,-467 606.5,-461 612.5,-461 612.5,-461 709.5,-461 709.5,-461 715.5,-461 721.5,-467 721.5,-473 721.5,-473 721.5,-532 721.5,-532 721.5,-538 715.5,-544 709.5,-544\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">类黄酮 &lt;= 1.496</text>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.414</text>\r\n",
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       "<g id=\"node4\" class=\"node\"><title>3</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"256\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">镁 &lt;= 84.024</text>\r\n",
       "<text text-anchor=\"middle\" x=\"256\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.242</text>\r\n",
       "<text text-anchor=\"middle\" x=\"256\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 25</text>\r\n",
       "<text text-anchor=\"middle\" x=\"256\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 24]</text>\r\n",
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       "<g id=\"node9\" class=\"node\"><title>8</title>\r\n",
       "<path fill=\"#39e581\" fill-opacity=\"0.164706\" stroke=\"black\" d=\"M504,-425C504,-425 412,-425 412,-425 406,-425 400,-419 400,-413 400,-413 400,-354 400,-354 400,-348 406,-342 412,-342 412,-342 504,-342 504,-342 510,-342 516,-348 516,-354 516,-354 516,-413 516,-413 516,-419 510,-425 504,-425\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"458\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">灰 &lt;= 2.569</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"458\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11</text>\r\n",
       "<text text-anchor=\"middle\" x=\"458\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 6, 5]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"458\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
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       "<path fill=\"#39e581\" fill-opacity=\"0.831373\" stroke=\"black\" d=\"M439,-306C439,-306 347,-306 347,-306 341,-306 335,-300 335,-294 335,-294 335,-235 335,-235 335,-229 341,-223 347,-223 347,-223 439,-223 439,-223 445,-223 451,-229 451,-235 451,-235 451,-294 451,-294 451,-300 445,-306 439,-306\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"393\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">苹果酸 &lt;= 3.37</text>\r\n",
       "<text text-anchor=\"middle\" x=\"393\" y=\"-275.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.592</text>\r\n",
       "<text text-anchor=\"middle\" x=\"393\" y=\"-260.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 7</text>\r\n",
       "<text text-anchor=\"middle\" x=\"393\" y=\"-245.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 6, 1]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"393\" y=\"-230.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 8&#45;&gt;9 -->\r\n",
       "<g id=\"edge9\" class=\"edge\"><title>8&#45;&gt;9</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M435.449,-341.907C430.609,-333.195 425.443,-323.897 420.441,-314.893\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"423.428,-313.063 415.512,-306.021 417.308,-316.462 423.428,-313.063\"/>\r\n",
       "</g>\r\n",
       "<!-- 14 -->\r\n",
       "<g id=\"node15\" class=\"node\"><title>14</title>\r\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M573,-298.5C573,-298.5 481,-298.5 481,-298.5 475,-298.5 469,-292.5 469,-286.5 469,-286.5 469,-242.5 469,-242.5 469,-236.5 475,-230.5 481,-230.5 481,-230.5 573,-230.5 573,-230.5 579,-230.5 585,-236.5 585,-242.5 585,-242.5 585,-286.5 585,-286.5 585,-292.5 579,-298.5 573,-298.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"527\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"527\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\r\n",
       "<text text-anchor=\"middle\" x=\"527\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 4]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"527\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 贝尔摩德</text>\r\n",
       "</g>\r\n",
       "<!-- 8&#45;&gt;14 -->\r\n",
       "<g id=\"edge14\" class=\"edge\"><title>8&#45;&gt;14</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M481.939,-341.907C488.512,-330.763 495.65,-318.658 502.267,-307.439\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"505.375,-309.059 507.44,-298.667 499.345,-305.503 505.375,-309.059\"/>\r\n",
       "</g>\r\n",
       "<!-- 10 -->\r\n",
       "<g id=\"node11\" class=\"node\"><title>10</title>\r\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M370.5,-179.5C370.5,-179.5 281.5,-179.5 281.5,-179.5 275.5,-179.5 269.5,-173.5 269.5,-167.5 269.5,-167.5 269.5,-123.5 269.5,-123.5 269.5,-117.5 275.5,-111.5 281.5,-111.5 281.5,-111.5 370.5,-111.5 370.5,-111.5 376.5,-111.5 382.5,-117.5 382.5,-123.5 382.5,-123.5 382.5,-167.5 382.5,-167.5 382.5,-173.5 376.5,-179.5 370.5,-179.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"326\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"326\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 5</text>\r\n",
       "<text text-anchor=\"middle\" x=\"326\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 5, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"326\" y=\"-119.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;10 -->\r\n",
       "<g id=\"edge10\" class=\"edge\"><title>9&#45;&gt;10</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M369.755,-222.907C363.373,-211.763 356.441,-199.658 350.016,-188.439\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"353,-186.606 344.993,-179.667 346.925,-190.084 353,-186.606\"/>\r\n",
       "</g>\r\n",
       "<!-- 11 -->\r\n",
       "<g id=\"node12\" class=\"node\"><title>11</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M501.5,-187C501.5,-187 412.5,-187 412.5,-187 406.5,-187 400.5,-181 400.5,-175 400.5,-175 400.5,-116 400.5,-116 400.5,-110 406.5,-104 412.5,-104 412.5,-104 501.5,-104 501.5,-104 507.5,-104 513.5,-110 513.5,-116 513.5,-116 513.5,-175 513.5,-175 513.5,-181 507.5,-187 501.5,-187\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"457\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">灰 &lt;= 2.349</text>\r\n",
       "<text text-anchor=\"middle\" x=\"457\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"457\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\r\n",
       "<text text-anchor=\"middle\" x=\"457\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 1]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"457\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 9&#45;&gt;11 -->\r\n",
       "<g id=\"edge11\" class=\"edge\"><title>9&#45;&gt;11</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M415.204,-222.907C419.97,-214.195 425.056,-204.897 429.981,-195.893\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"433.106,-197.474 434.835,-187.021 426.965,-194.114 433.106,-197.474\"/>\r\n",
       "</g>\r\n",
       "<!-- 12 -->\r\n",
       "<g id=\"node13\" class=\"node\"><title>12</title>\r\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M434.5,-68C434.5,-68 345.5,-68 345.5,-68 339.5,-68 333.5,-62 333.5,-56 333.5,-56 333.5,-12 333.5,-12 333.5,-6 339.5,-0 345.5,-0 345.5,-0 434.5,-0 434.5,-0 440.5,-0 446.5,-6 446.5,-12 446.5,-12 446.5,-56 446.5,-56 446.5,-62 440.5,-68 434.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"390\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"390\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\r\n",
       "<text text-anchor=\"middle\" x=\"390\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"390\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 11&#45;&gt;12 -->\r\n",
       "<g id=\"edge12\" class=\"edge\"><title>11&#45;&gt;12</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M432.052,-103.726C426.694,-94.9703 421.024,-85.7032 415.641,-76.9051\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"418.58,-75.0028 410.375,-68.2996 412.609,-78.6563 418.58,-75.0028\"/>\r\n",
       "</g>\r\n",
       "<!-- 13 -->\r\n",
       "<g id=\"node14\" class=\"node\"><title>13</title>\r\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M569,-68C569,-68 477,-68 477,-68 471,-68 465,-62 465,-56 465,-56 465,-12 465,-12 465,-6 471,-0 477,-0 477,-0 569,-0 569,-0 575,-0 581,-6 581,-12 581,-12 581,-56 581,-56 581,-62 575,-68 569,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"523\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"523\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\r\n",
       "<text text-anchor=\"middle\" x=\"523\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 1]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"523\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 贝尔摩德</text>\r\n",
       "</g>\r\n",
       "<!-- 11&#45;&gt;13 -->\r\n",
       "<g id=\"edge13\" class=\"edge\"><title>11&#45;&gt;13</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M481.576,-103.726C486.854,-94.9703 492.439,-85.7032 497.742,-76.9051\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"500.764,-78.671 502.929,-68.2996 494.769,-75.0574 500.764,-78.671\"/>\r\n",
       "</g>\r\n",
       "<!-- 16 -->\r\n",
       "<g id=\"node17\" class=\"node\"><title>16</title>\r\n",
       "<path fill=\"#39e581\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M707,-425C707,-425 615,-425 615,-425 609,-425 603,-419 603,-413 603,-413 603,-354 603,-354 603,-348 609,-342 615,-342 615,-342 707,-342 707,-342 713,-342 719,-348 719,-354 719,-354 719,-413 719,-413 719,-419 713,-425 707,-425\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">色调 &lt;= 0.854</text>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 6</text>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-364.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 4, 2]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-349.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 15&#45;&gt;16 -->\r\n",
       "<g id=\"edge16\" class=\"edge\"><title>15&#45;&gt;16</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M661,-460.907C661,-452.649 661,-443.864 661,-435.302\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"664.5,-435.021 661,-425.021 657.5,-435.021 664.5,-435.021\"/>\r\n",
       "</g>\r\n",
       "<!-- 19 -->\r\n",
       "<g id=\"node20\" class=\"node\"><title>19</title>\r\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M846.5,-417.5C846.5,-417.5 749.5,-417.5 749.5,-417.5 743.5,-417.5 737.5,-411.5 737.5,-405.5 737.5,-405.5 737.5,-361.5 737.5,-361.5 737.5,-355.5 743.5,-349.5 749.5,-349.5 749.5,-349.5 846.5,-349.5 846.5,-349.5 852.5,-349.5 858.5,-355.5 858.5,-361.5 858.5,-361.5 858.5,-405.5 858.5,-405.5 858.5,-411.5 852.5,-417.5 846.5,-417.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"798\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"798\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 18</text>\r\n",
       "<text text-anchor=\"middle\" x=\"798\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 18, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"798\" y=\"-357.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 15&#45;&gt;19 -->\r\n",
       "<g id=\"edge19\" class=\"edge\"><title>15&#45;&gt;19</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M708.531,-460.907C722.357,-449.101 737.443,-436.217 751.221,-424.45\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"753.832,-426.823 759.164,-417.667 749.286,-421.5 753.832,-426.823\"/>\r\n",
       "</g>\r\n",
       "<!-- 17 -->\r\n",
       "<g id=\"node18\" class=\"node\"><title>17</title>\r\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M707,-298.5C707,-298.5 615,-298.5 615,-298.5 609,-298.5 603,-292.5 603,-286.5 603,-286.5 603,-242.5 603,-242.5 603,-236.5 609,-230.5 615,-230.5 615,-230.5 707,-230.5 707,-230.5 713,-230.5 719,-236.5 719,-242.5 719,-242.5 719,-286.5 719,-286.5 719,-292.5 713,-298.5 707,-298.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 2]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"661\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 贝尔摩德</text>\r\n",
       "</g>\r\n",
       "<!-- 16&#45;&gt;17 -->\r\n",
       "<g id=\"edge17\" class=\"edge\"><title>16&#45;&gt;17</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M661,-341.907C661,-331.204 661,-319.615 661,-308.776\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"664.5,-308.667 661,-298.667 657.5,-308.667 664.5,-308.667\"/>\r\n",
       "</g>\r\n",
       "<!-- 18 -->\r\n",
       "<g id=\"node19\" class=\"node\"><title>18</title>\r\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M838.5,-298.5C838.5,-298.5 749.5,-298.5 749.5,-298.5 743.5,-298.5 737.5,-292.5 737.5,-286.5 737.5,-286.5 737.5,-242.5 737.5,-242.5 737.5,-236.5 743.5,-230.5 749.5,-230.5 749.5,-230.5 838.5,-230.5 838.5,-230.5 844.5,-230.5 850.5,-236.5 850.5,-242.5 850.5,-242.5 850.5,-286.5 850.5,-286.5 850.5,-292.5 844.5,-298.5 838.5,-298.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"794\" y=\"-283.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"794\" y=\"-268.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\r\n",
       "<text text-anchor=\"middle\" x=\"794\" y=\"-253.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 4, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"794\" y=\"-238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 16&#45;&gt;18 -->\r\n",
       "<g id=\"edge18\" class=\"edge\"><title>16&#45;&gt;18</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M707.144,-341.907C720.565,-330.101 735.211,-317.217 748.587,-305.45\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"751.101,-307.9 756.297,-298.667 746.477,-302.644 751.101,-307.9\"/>\r\n",
       "</g>\r\n",
       "<!-- 21 -->\r\n",
       "<g id=\"node22\" class=\"node\"><title>21</title>\r\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M876.5,-536.5C876.5,-536.5 779.5,-536.5 779.5,-536.5 773.5,-536.5 767.5,-530.5 767.5,-524.5 767.5,-524.5 767.5,-480.5 767.5,-480.5 767.5,-474.5 773.5,-468.5 779.5,-468.5 779.5,-468.5 876.5,-468.5 876.5,-468.5 882.5,-468.5 888.5,-474.5 888.5,-480.5 888.5,-480.5 888.5,-524.5 888.5,-524.5 888.5,-530.5 882.5,-536.5 876.5,-536.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"828\" y=\"-521.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"828\" y=\"-506.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\r\n",
       "<text text-anchor=\"middle\" x=\"828\" y=\"-491.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 16, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"828\" y=\"-476.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 20&#45;&gt;21 -->\r\n",
       "<g id=\"edge21\" class=\"edge\"><title>20&#45;&gt;21</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M837.143,-579.907C835.862,-569.204 834.475,-557.615 833.178,-546.776\"/>\r\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"836.632,-546.18 831.969,-536.667 829.682,-547.012 836.632,-546.18\"/>\r\n",
       "</g>\r\n",
       "<!-- 22 -->\r\n",
       "<g id=\"node23\" class=\"node\"><title>22</title>\r\n",
       "<path fill=\"#e58139\" fill-opacity=\"0.858824\" stroke=\"black\" d=\"M1015.5,-544C1015.5,-544 918.5,-544 918.5,-544 912.5,-544 906.5,-538 906.5,-532 906.5,-532 906.5,-473 906.5,-473 906.5,-467 912.5,-461 918.5,-461 918.5,-461 1015.5,-461 1015.5,-461 1021.5,-461 1027.5,-467 1027.5,-473 1027.5,-473 1027.5,-532 1027.5,-532 1027.5,-538 1021.5,-544 1015.5,-544\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"967\" y=\"-528.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">酒精 &lt;= 12.731</text>\r\n",
       "<text text-anchor=\"middle\" x=\"967\" y=\"-513.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.544</text>\r\n",
       "<text text-anchor=\"middle\" x=\"967\" y=\"-498.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 48</text>\r\n",
       "<text text-anchor=\"middle\" x=\"967\" y=\"-483.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [42, 6, 0]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"967\" y=\"-468.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 琴酒</text>\r\n",
       "</g>\r\n",
       "<!-- 20&#45;&gt;22 -->\r\n",
       "<g id=\"edge22\" class=\"edge\"><title>20&#45;&gt;22</title>\r\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M885.368,-579.907C895.355,-570.56 906.062,-560.538 916.328,-550.929\"/>\r\n",
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       "<!-- 23 -->\r\n",
       "<g id=\"node24\" class=\"node\"><title>23</title>\r\n",
       "<path fill=\"#39e581\" stroke=\"black\" d=\"M994.5,-417.5C994.5,-417.5 905.5,-417.5 905.5,-417.5 899.5,-417.5 893.5,-411.5 893.5,-405.5 893.5,-405.5 893.5,-361.5 893.5,-361.5 893.5,-355.5 899.5,-349.5 905.5,-349.5 905.5,-349.5 994.5,-349.5 994.5,-349.5 1000.5,-349.5 1006.5,-355.5 1006.5,-361.5 1006.5,-361.5 1006.5,-405.5 1006.5,-405.5 1006.5,-411.5 1000.5,-417.5 994.5,-417.5\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"950\" y=\"-402.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"950\" y=\"-387.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 2</text>\r\n",
       "<text text-anchor=\"middle\" x=\"950\" y=\"-372.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 2, 0]</text>\r\n",
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       "</g>\r\n",
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       "<text text-anchor=\"middle\" x=\"1085\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">镁 &lt;= 87.478</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1085\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.426</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"1156\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">灰的碱性 &lt;= 28.531</text>\r\n",
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       "<!-- 24&#45;&gt;26 -->\r\n",
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       "<!-- 26&#45;&gt;27 -->\r\n",
       "<g id=\"edge27\" class=\"edge\"><title>26&#45;&gt;27</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"1223\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"1223\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\r\n",
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       "</g>\r\n",
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       "<g id=\"edge28\" class=\"edge\"><title>26&#45;&gt;28</title>\r\n",
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       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x23f89ee5588>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "dot_data = tree.export_graphviz(clf\n",
    "                                ,feature_names= feature_name\n",
    "                                ,class_names=[\"琴酒\",\"雪莉\",\"贝尔摩德\"]\n",
    "                                ,filled=True\n",
    "                                ,rounded=True\n",
    "                                )\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#我们的树对训练集的拟合程度如何？\n",
    "score_train = clf.score(Xtrain, Ytrain)\n",
    "score_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<text text-anchor=\"middle\" x=\"418.5\" y=\"-409.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">类黄酮 &lt;= 2.154</text>\r\n",
       "<text text-anchor=\"middle\" x=\"418.5\" y=\"-394.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.556</text>\r\n",
       "<text text-anchor=\"middle\" x=\"418.5\" y=\"-379.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 124</text>\r\n",
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       "<text text-anchor=\"middle\" x=\"517.5\" y=\"-290.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">脯氨酸 &lt;= 558.218</text>\r\n",
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       "</g>\r\n",
       "<!-- 2&#45;&gt;3 -->\r\n",
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       "<!-- 4 -->\r\n",
       "<g id=\"node5\" class=\"node\"><title>4</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"197.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 11</text>\r\n",
       "<text text-anchor=\"middle\" x=\"197.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 6, 5]</text>\r\n",
       "<text text-anchor=\"middle\" x=\"197.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 2&#45;&gt;4 -->\r\n",
       "<g id=\"edge4\" class=\"edge\"><title>2&#45;&gt;4</title>\r\n",
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       "<!-- 6 -->\r\n",
       "<g id=\"node7\" class=\"node\"><title>6</title>\r\n",
       "<path fill=\"#8139e5\" fill-opacity=\"0.498039\" stroke=\"black\" d=\"M379.5,-68C379.5,-68 287.5,-68 287.5,-68 281.5,-68 275.5,-62 275.5,-56 275.5,-56 275.5,-12 275.5,-12 275.5,-6 281.5,-0 287.5,-0 287.5,-0 379.5,-0 379.5,-0 385.5,-0 391.5,-6 391.5,-12 391.5,-12 391.5,-56 391.5,-56 391.5,-62 385.5,-68 379.5,-68\"/>\r\n",
       "<text text-anchor=\"middle\" x=\"333.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\r\n",
       "<text text-anchor=\"middle\" x=\"333.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\r\n",
       "<text text-anchor=\"middle\" x=\"333.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 1, 2]</text>\r\n",
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       "</g>\r\n",
       "<!-- 5&#45;&gt;6 -->\r\n",
       "<g id=\"edge6\" class=\"edge\"><title>5&#45;&gt;6</title>\r\n",
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       "<g id=\"node8\" class=\"node\"><title>7</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"470.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 雪莉</text>\r\n",
       "</g>\r\n",
       "<!-- 5&#45;&gt;7 -->\r\n",
       "<g id=\"edge7\" class=\"edge\"><title>5&#45;&gt;7</title>\r\n",
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       "<g id=\"node10\" class=\"node\"><title>9</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"502.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.0</text>\r\n",
       "<text text-anchor=\"middle\" x=\"502.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 14</text>\r\n",
       "<text text-anchor=\"middle\" x=\"502.5\" y=\"-134.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 14, 0]</text>\r\n",
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       "</g>\r\n",
       "<!-- 8&#45;&gt;9 -->\r\n",
       "<g id=\"edge9\" class=\"edge\"><title>8&#45;&gt;9</title>\r\n",
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       "<g id=\"node11\" class=\"node\"><title>10</title>\r\n",
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       "<text text-anchor=\"middle\" x=\"641.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">酒精 &lt;= 12.689</text>\r\n",
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       "<!-- 8&#45;&gt;10 -->\r\n",
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       "</svg>\r\n"
      ],
      "text/plain": [
       "<graphviz.files.Source at 0x23f89ea8eb8>"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = tree.DecisionTreeClassifier(criterion=\"entropy\"\n",
    "                                    ,random_state=30\n",
    "                                    ,splitter=\"random\"\n",
    "                                    ,max_depth=3\n",
    "                                #    ,min_samples_leaf=10\n",
    "                                #    ,min_samples_split=25\n",
    "                                    )\n",
    "clf = clf.fit(Xtrain, Ytrain)\n",
    "dot_data = tree.export_graphviz(clf\n",
    "                                ,feature_names= feature_name\n",
    "                                ,class_names=[\"琴酒\",\"雪莉\",\"贝尔摩德\"]\n",
    "                                ,filled=True\n",
    "                                ,rounded=True\n",
    "                                )\n",
    "graph = graphviz.Source(dot_data)\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9814814814814815"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = clf.score(Xtest, Ytest)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "test = []\n",
    "for i in range(10):\n",
    "    clf = tree.DecisionTreeClassifier(max_depth=i+1\n",
    "                                    ,criterion=\"entropy\"\n",
    "                                    ,random_state=30\n",
    "                                    ,splitter=\"random\"\n",
    "                                    )\n",
    "    clf = clf.fit(Xtrain, Ytrain)\n",
    "    score = clf.score(Xtest, Ytest)\n",
    "    test.append(score)\n",
    "plt.plot(range(1,11),test,color=\"red\",label=\"max_depth\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 27, 19, 27,  7,  7,  7, 27, 19, 27, 21, 21,  7, 18,  7, 21,  7,\n",
       "       10,  7, 19,  7, 27, 19, 19, 27, 21,  7, 21, 27,  7, 19,  7,  7, 27,\n",
       "       21, 27, 27,  7, 27,  7, 19, 27,  7, 27, 27, 19, 19, 10, 27, 27, 27,\n",
       "       10,  7, 21], dtype=int64)"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#apply返回每个测试样本所在的叶子节点的索引\n",
    "clf.apply(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 2, 2, 2, 0, 1, 0, 1, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 0,\n",
       "       1, 1, 0, 1, 2, 1, 0, 2, 1, 2, 2, 0, 1, 0, 0, 2, 0, 2, 1, 0, 2, 0,\n",
       "       0, 1, 1, 1, 0, 0, 0, 1, 2, 1])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#predict返回每个测试样本的分类/回归结果\n",
    "clf.predict(Xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
       "        4.9800e+00],\n",
       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
       "        9.1400e+00],\n",
       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
       "        4.0300e+00],\n",
       "       ...,\n",
       "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        5.6400e+00],\n",
       "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
       "        6.4800e+00],\n",
       "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        7.8800e+00]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.1253505322812249"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = DecisionTreeRegressor(random_state=0) #实例化\n",
    "cross_val_score(regressor, boston.data, boston.target, cv=10).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n",
       "        4.9800e+00],\n",
       "       [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n",
       "        9.1400e+00],\n",
       "       [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n",
       "        4.0300e+00],\n",
       "       ...,\n",
       "       [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        5.6400e+00],\n",
       "       [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n",
       "        6.4800e+00],\n",
       "       [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n",
       "        7.8800e+00]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([24. , 21.6, 34.7, 33.4, 36.2, 28.7, 22.9, 27.1, 16.5, 18.9, 15. ,\n",
       "       18.9, 21.7, 20.4, 18.2, 19.9, 23.1, 17.5, 20.2, 18.2, 13.6, 19.6,\n",
       "       15.2, 14.5, 15.6, 13.9, 16.6, 14.8, 18.4, 21. , 12.7, 14.5, 13.2,\n",
       "       13.1, 13.5, 18.9, 20. , 21. , 24.7, 30.8, 34.9, 26.6, 25.3, 24.7,\n",
       "       21.2, 19.3, 20. , 16.6, 14.4, 19.4, 19.7, 20.5, 25. , 23.4, 18.9,\n",
       "       35.4, 24.7, 31.6, 23.3, 19.6, 18.7, 16. , 22.2, 25. , 33. , 23.5,\n",
       "       19.4, 22. , 17.4, 20.9, 24.2, 21.7, 22.8, 23.4, 24.1, 21.4, 20. ,\n",
       "       20.8, 21.2, 20.3, 28. , 23.9, 24.8, 22.9, 23.9, 26.6, 22.5, 22.2,\n",
       "       23.6, 28.7, 22.6, 22. , 22.9, 25. , 20.6, 28.4, 21.4, 38.7, 43.8,\n",
       "       33.2, 27.5, 26.5, 18.6, 19.3, 20.1, 19.5, 19.5, 20.4, 19.8, 19.4,\n",
       "       21.7, 22.8, 18.8, 18.7, 18.5, 18.3, 21.2, 19.2, 20.4, 19.3, 22. ,\n",
       "       20.3, 20.5, 17.3, 18.8, 21.4, 15.7, 16.2, 18. , 14.3, 19.2, 19.6,\n",
       "       23. , 18.4, 15.6, 18.1, 17.4, 17.1, 13.3, 17.8, 14. , 14.4, 13.4,\n",
       "       15.6, 11.8, 13.8, 15.6, 14.6, 17.8, 15.4, 21.5, 19.6, 15.3, 19.4,\n",
       "       17. , 15.6, 13.1, 41.3, 24.3, 23.3, 27. , 50. , 50. , 50. , 22.7,\n",
       "       25. , 50. , 23.8, 23.8, 22.3, 17.4, 19.1, 23.1, 23.6, 22.6, 29.4,\n",
       "       23.2, 24.6, 29.9, 37.2, 39.8, 36.2, 37.9, 32.5, 26.4, 29.6, 50. ,\n",
       "       32. , 29.8, 34.9, 37. , 30.5, 36.4, 31.1, 29.1, 50. , 33.3, 30.3,\n",
       "       34.6, 34.9, 32.9, 24.1, 42.3, 48.5, 50. , 22.6, 24.4, 22.5, 24.4,\n",
       "       20. , 21.7, 19.3, 22.4, 28.1, 23.7, 25. , 23.3, 28.7, 21.5, 23. ,\n",
       "       26.7, 21.7, 27.5, 30.1, 44.8, 50. , 37.6, 31.6, 46.7, 31.5, 24.3,\n",
       "       31.7, 41.7, 48.3, 29. , 24. , 25.1, 31.5, 23.7, 23.3, 22. , 20.1,\n",
       "       22.2, 23.7, 17.6, 18.5, 24.3, 20.5, 24.5, 26.2, 24.4, 24.8, 29.6,\n",
       "       42.8, 21.9, 20.9, 44. , 50. , 36. , 30.1, 33.8, 43.1, 48.8, 31. ,\n",
       "       36.5, 22.8, 30.7, 50. , 43.5, 20.7, 21.1, 25.2, 24.4, 35.2, 32.4,\n",
       "       32. , 33.2, 33.1, 29.1, 35.1, 45.4, 35.4, 46. , 50. , 32.2, 22. ,\n",
       "       20.1, 23.2, 22.3, 24.8, 28.5, 37.3, 27.9, 23.9, 21.7, 28.6, 27.1,\n",
       "       20.3, 22.5, 29. , 24.8, 22. , 26.4, 33.1, 36.1, 28.4, 33.4, 28.2,\n",
       "       22.8, 20.3, 16.1, 22.1, 19.4, 21.6, 23.8, 16.2, 17.8, 19.8, 23.1,\n",
       "       21. , 23.8, 23.1, 20.4, 18.5, 25. , 24.6, 23. , 22.2, 19.3, 22.6,\n",
       "       19.8, 17.1, 19.4, 22.2, 20.7, 21.1, 19.5, 18.5, 20.6, 19. , 18.7,\n",
       "       32.7, 16.5, 23.9, 31.2, 17.5, 17.2, 23.1, 24.5, 26.6, 22.9, 24.1,\n",
       "       18.6, 30.1, 18.2, 20.6, 17.8, 21.7, 22.7, 22.6, 25. , 19.9, 20.8,\n",
       "       16.8, 21.9, 27.5, 21.9, 23.1, 50. , 50. , 50. , 50. , 50. , 13.8,\n",
       "       13.8, 15. , 13.9, 13.3, 13.1, 10.2, 10.4, 10.9, 11.3, 12.3,  8.8,\n",
       "        7.2, 10.5,  7.4, 10.2, 11.5, 15.1, 23.2,  9.7, 13.8, 12.7, 13.1,\n",
       "       12.5,  8.5,  5. ,  6.3,  5.6,  7.2, 12.1,  8.3,  8.5,  5. , 11.9,\n",
       "       27.9, 17.2, 27.5, 15. , 17.2, 17.9, 16.3,  7. ,  7.2,  7.5, 10.4,\n",
       "        8.8,  8.4, 16.7, 14.2, 20.8, 13.4, 11.7,  8.3, 10.2, 10.9, 11. ,\n",
       "        9.5, 14.5, 14.1, 16.1, 14.3, 11.7, 13.4,  9.6,  8.7,  8.4, 12.8,\n",
       "       10.5, 17.1, 18.4, 15.4, 10.8, 11.8, 14.9, 12.6, 14.1, 13. , 13.4,\n",
       "       15.2, 16.1, 17.8, 14.9, 14.1, 12.7, 13.5, 14.9, 20. , 16.4, 17.7,\n",
       "       19.5, 20.2, 21.4, 19.9, 19. , 19.1, 19.1, 20.1, 19.9, 19.6, 23.2,\n",
       "       29.8, 13.8, 13.3, 16.7, 12. , 14.6, 21.4, 23. , 23.7, 25. , 21.8,\n",
       "       20.6, 21.2, 19.1, 20.6, 15.2,  7. ,  8.1, 13.6, 20.1, 21.8, 24.5,\n",
       "       23.1, 19.7, 18.3, 21.2, 17.5, 16.8, 22.4, 20.6, 23.9, 22. , 11.9])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.tree import DecisionTreeRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-16.41568627, -10.61843137, -18.30176471, -55.36803922,\n",
       "       -16.01470588, -44.70117647, -12.2148    , -91.3888    ,\n",
       "       -57.764     , -36.8134    ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regressor = DecisionTreeRegressor(random_state=0)\n",
    "cross_val_score(regressor, boston.data, boston.target, cv=10,\n",
    "                scoring = \"neg_mean_squared_error\"\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(1) #随机数种子\n",
    "X = np.sort(5 * rng.rand(80,1), axis=0) #生成0~5之间随机的x的取值\n",
    "y = np.sin(X).ravel() #生成正弦曲线\n",
    "y[::5] += 3 * (0.5 - rng.rand(16)) #在正弦曲线上加噪声"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80,)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x214e60b7898>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(X, y, s=20, edgecolor=\"black\",c=\"darkorange\", label=\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(criterion='mse', max_depth=5, max_features=None,\n",
       "           max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
       "           min_impurity_split=None, min_samples_leaf=1,\n",
       "           min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "           presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr_1 = DecisionTreeRegressor(max_depth=2)\n",
    "regr_2 = DecisionTreeRegressor(max_depth=5)\n",
    "regr_1.fit(X, y)\n",
    "regr_2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1 = regr_1.predict(X_test)\n",
    "y_2 = regr_2.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.scatter(X, y, s=20, edgecolor=\"black\",c=\"darkorange\", label=\"data\")\n",
    "plt.plot(X_test, y_1, color=\"cornflowerblue\",label=\"max_depth=2\", linewidth=2)\n",
    "plt.plot(X_test, y_2, color=\"yellowgreen\", label=\"max_depth=5\", linewidth=2)\n",
    "plt.xlabel(\"data\")\n",
    "plt.ylabel(\"target\")\n",
    "plt.title(\"Decision Tree Regression\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
